 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q5H8B9 from www.uniprot.org...
The NucPred score for your sequence is 0.29 (see score help below)
1 MAGDSLSLPGMSLQLLVTLTCLLLTCALRERVPLSENVSHCELYLPPWGA 50
51 LNGGSHQNPGILIANSGFQVPQGRSVWLNPLRDLVIRVQRGDQCKVTVLD 100
101 FPRLQGALSPHQFPCDFGAQQVKYTHFGSPSSTRTRIQLQVRYDAGNSTL 150
151 VLPFTLQVNVVFPKLQLVARNRPLKVFKVLGCSHAIDRRVLNFASRSRCR 200
201 LTVLPHPGGPLPKYGLLVDTAGNPLSRGHLADCEAFIQAGVRYQHTTTPS 250
251 SPSRDYVPMLVELLGPESQGTRSVEVLAQEYFQIQVRIQKRARSTAPIPS 300
301 LAALMIVEVEQSLLTALTPDVLAAEDSESDPDDLVFNILNVPEAPPGHHG 350
351 GQGYVVNTDDPLGLPVSFFTQKELRELKIAYRPPTGSSEGDHVFQLKLQV 400
401 VDEDGDTSEPFAFMVVVKSMNVLAPIASYRGGLVVFEGQARPLSDALSFQ 450
451 VSDKDNSKEIKIVAVRGLQHGQLVVHGAPAGCKNFTLADLSAGRVVYQHD 500
501 GSDSYSDNIILKMEGEHHRVDFLFPITIVPVDDAAPVLTANMGLSVTEGQ 550
551 VVQISPFVLCATDIDSEDSAILFVLEDEHLEGKEEETHEDLAPGSYSSSQ 600
601 HPGNMLLRQAEPPSSLLYSDWHYVEKEGLYETVVTEWLQRDIMEGRLFFS 650
651 HPGPHSPSPVAHLAFHVQDDQDPPNLSNQHFFTISIQPADTSQPQLSPET 700
701 TLEMTVQGYQLTPFQQKYLQYTDQNSDEQNLWYTLLTRPTDADSNHQVQA 750
751 GEIVLTDSPGRPIVHFTQTQVNHQKIAYRPPQRNLGIVPRVVQFTYKVED 800
801 TAGNSVSGTFTVFLQPLNNQPPKVINRGFAVLEGDSFILGRGELNVSDPD 850
851 TNDDQIFFILVWGPQHGHLQYFKKYMVPGESFMLADVINGSISYQNSRDQ 900
901 TDSDVIYLEVSDGVHHIPITVQVSVQPTVADRSPGISITGTPVLATSLTV 950
951 LENAASEITMDVTHGRKNTNDLMLSFIVEDKPKLGMILVNGWPAEQVTQE 1000
1001 DLLGGAVSYVHTSGEIGFQRVHDTFSFILSKDAYHRAMRDNILERVLVEV 1050
1051 TVLPVDNMAPKVLVRKPFIVYEGGKNLLTPQHVNIEDVDTSKDEILCTLT 1100
1101 VQPSSGYLENLAPAPGSEVSRAGNPISAFFVKDVRVGCINYVQSIHKGIE 1150
1151 PGADQFTFYCSDGINFSPKVLLPLTILPTNDEQPQLFTREFVVLEGMSRV 1200
1201 IDTSLVNGVDADFPSDKHFRLTVFPQHGQITQQLATGSKPIHSFTLQEIQ 1250
1251 EPSSIAYEHDDSESTEDSFEVWLSDGRHTTHTTVPIAVILVDDEVPQLTI 1300
1301 NDGLEIETGHSEIITNHILKAIDLDSDNKSLSFVLHSEPQQGLLQRLRKP 1350
1351 GGDAKHNLTVGMNFTQDEIDRGLIHYIHTGQGEAVDLKFDVTDGVNTLRD 1400
1401 RYFYITIDNSNSAVPEIVSKRVTLTEGNRMTLTTELLNTSDIHSPGEQLL 1450
1451 YSITRAPSTGHLESSDQPGEPMASFTQLQLARNKISYVHISNDKIKLDHF 1500
1501 ELRVTGGHHSESRIFRIFVTEQDNKKPTLTIQALALQRGDNIVVTPSQLT 1550
1551 VEDEDTPADFILFTITQIPIHGRILYNGSRPVTTFTKKDLTKSLIYCHDG 1600
1601 SETSKDSFSFTVTDGIHTGFYVFPDTSLETHVPQTVWIQISPFDDRLPQM 1650
1651 GINRGATALKLLHTGHLGFLITNEYLQATHQGVPHRLLTYKVTRGPEHGY 1700
1701 IVNAGLGNENTHMFTQADIDEMRVYYILNKGRGRATRDTFYFSVETRGGK 1750
1751 QLRNQPFHLNWAWISLEKEYYIVDEDSPFFEVTLRRGYLGGTSVVSIGTK 1800
1801 GDTAEENKDFKGKAPMLVQFSPGQSTATWRVGLIPDTKYETSETFQIILS 1850
1851 EPGAAALEFPEMATVEIVDPEDESTVYIPEAEYQLAEAVGEFLVPVRRSG 1900
1901 DTSQELTVICSTRPGSATGTIPSVQLSLSDYVSRPEDNTSMLHFEKDESE 1950
1951 KTCQVLIINDSLYEEEESFSIALSLPTGGQLGAKFPTARVTILADREDEP 2000
2001 ALHFECSEYHAEESAGYVEVAVWRRGTGLSQASSVIVRSRGTEEQAAEAG 2050
2051 TDYIGVRQHLHFSPGVSVQRLRVTILDDLPQPAFEGPEMFELLLQMPTGA 2100
2101 VIGEPNKTTVTINDSVTNCEECV 2123
Positively and negatively influencing subsequences are coloured according to the following scale:
(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)
What does the NucPred score mean?
| You have to decide on a NucPred score threshold. Sequences which score greater than or equal to this threshold are predicted to spend some time in the nucleus. Higher thresholds yield fewer predicted nuclear proteins, but these predictions are more accurate (you can have higher confidence in them). The table below gives more details of the performance of NucPred estimated using the sequences it was trained on (by cross-validation). Another benchmark is available in the Bioinformatics 2007 paper. |
| NucPred score threshold | Specificity | Sensitivity |
| see above | fraction of proteins predicted to be nuclear that actually are nuclear | fraction of true nuclear proteins that are predicted (coverage) |
| 0.10 | 0.45 | 0.88 |
| 0.20 | 0.52 | 0.83 |
| 0.30 | 0.57 | 0.77 |
| 0.40 | 0.63 | 0.69 |
| 0.50 | 0.70 | 0.62 |
| 0.60 | 0.71 | 0.53 |
| 0.70 | 0.81 | 0.44 |
| 0.80 | 0.84 | 0.32 |
| 0.90 | 0.88 | 0.21 |
| 1.00 | 1.00 | 0.02 |
| Sequences which score >= 0.8 with NucPred and which
are predicted by PredictNLS to contain an NLS have been shown to be 93% correct with a coverage of 16%. (PredictNLS by itself is 87% correct with 26% coverage on the same data.) |
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